A Spatio-Temporal Graph Learning Approach to Real-Time Economic Dispatch with Multi-Transmission-Node DER Aggregation

TL;DR

This paper proposes a spatio-temporal graph learning framework for real-time economic dispatch that efficiently integrates multi-transmission-node DERs, improving operational costs and system reliability.

Contribution

It introduces a novel ST-GCN model for dynamic distribution factor prediction and an iterative constraint strategy for transmission security, enhancing dispatch efficiency.

Findings

Reduces operational costs significantly in large-scale test systems.

Maintains transmission network feasibility under real demand scenarios.

Accelerates market clearing processes with scalable solutions.

Abstract

The integration of distributed energy resources (DERs) into wholesale electricity markets, as mandated by FERC Order 2222, imposes new challenges on system operations. To remain consistent with existing market structures, regional transmission organizations (RTOs) have advanced the aggregation of transmission-node-level DERs (T-DERs), where a nodal virtual power plant (VPP) represents the mapping of all distribution-level DERs to their respective transmission nodes. This paper develops a real-time economic dispatch (RTED) framework that enables multi-transmission-node DER aggregation while addressing computational efficiency. To this end, we introduce a spatio-temporal graph convolutional network (ST-GCN) for adaptive prediction of distribution factors (DFs), thereby capturing the dynamic influence of individual T-DERs across the transmission system. Furthermore, an iterative constraint identification strategy is incorporated to alleviate transmission security constraints without compromising system reliability. Together, these innovations accelerate the market clearing process and support the effective participation of T-DER aggregators under current market paradigms. The proposed approach is validated on large-scale test systems, including modified 118-, 2383-, and 3012-bus networks under a rolling RTED setting with real demand data. Numerical results demonstrate significant improvements in reducing operational costs and maintaining transmission network feasibility, underscoring the scalability and practicality of the proposed framework.